forked from Archives/langchain
46 lines
1.9 KiB
Markdown
46 lines
1.9 KiB
Markdown
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# Unstructured
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This page covers how to use the [`unstructured`](https://github.com/Unstructured-IO/unstructured)
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ecosystem within LangChain. The `unstructured` package from
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[Unstructured.IO](https://www.unstructured.io/) extracts clean text from raw source documents like
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PDFs and Word documents.
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This page is broken into two parts: installation and setup, and then references to specific
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`unstructured` wrappers.
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## Installation and Setup
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- Install the Python SDK with `pip install unstructured[local-inference]`
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- Install the following system dependencies if they are not already available on your system.
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Depending on what document types you're parsing, you may not need all of these.
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- `libmagic-dev`
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- `poppler-utils`
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- `tesseract-ocr`
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- `libreoffice`
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- Run the following to install NLTK dependencies. `unstructured` will handle this automatically
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soon.
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- `python -c "import nltk; nltk.download('punkt')"`
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- `python -c "import nltk; nltk.download('averaged_perceptron_tagger')"`
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- If you are parsing PDFs, run the following to install the `detectron2` model, which
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`unstructured` uses for layout detection:
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- `pip install "detectron2@git+https://github.com/facebookresearch/detectron2.git@v0.6#egg=detectron2"`
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## Wrappers
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### Data Loaders
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The primary `unstructured` wrappers within `langchain` are data loaders. The following
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shows how to use the most basic unstructured data loader. There are other file-specific
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data loaders available in the `langchain.document_loaders` module.
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```python
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from langchain.document_loaders import UnstructuredFileLoader
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loader = UnstructuredFileLoader("state_of_the_union.txt")
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loader.load()
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```
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If you instantiate the loader with `UnstructuredFileLoader(mode="elements")`, the loader
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will track additional metadata like the page number and text type (i.e. title, narrative text)
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when that information is available.
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